Discovering Association Rules and Classification for Biological Data using Data Mining Methods

摘要

This project presents a set of algorithms and their efficiency for discovering association rules and classification using Genetics Algorithm (GA), Decision Trees (DT) and Neural Networks (NN). A GA generates a large set of possible solutions to a given problem. Apriori is the basic algorithm for association rules. A GA is developed for finding the frequent conditions. The proposed GA based on encoding and generation construction method (GA_EN) can mine association rules with improved performance using appropriate generation of the rules. For GA classification (GA_CL) algorithm, rules are classified using predefined constraints. A Decision Tree algorithm (DTA) is created from data using probabilities, and the goal is to create on-demand an accurate decision tree (DT). Based on the rules produced from GA_CL, a Neural Network classifier (NNC_GA) is created. For learning a backpropagation neural network algorithm is used to adjust the weights. Simulation results are provided.